Structurally optimized neural fuzzy modelling for model predictive control
journal contributionposted on 2021-12-13, 16:13 authored by Xiaoyan Hu, Yu GongYu Gong, Dezong Zhao, Wen Gu
This paper investigates the local linear model tree (LOLIMOT), a typical neural fuzzy model, in the multiple-input-multiple-output model predictive control (MPC). In the conventional LOLIMOT, the structural parameters including centres and variances of its Gaussian kernels are set based on equally dividing the input data space. In this paper, after the structural parameters are initially obtained from the input space partition, they are optimized by the gradient descent search, from which the space partitions are further adjusted. This makes it better for the model structure to fit the input data statistics, leading to improved modelling performance with small model size. The MPC based on the proposed structurally optimized LOLIMOT is then implemented and verified with both numerical and diesel engine plants. Validation results show that the proposed MPC has significantly better controlling performance than the MPC based on the conventional LOLIMOT, making it an attractive solution in practice.
Towards Energy Efficient Autonomous Vehicles via Cloud-Aided Learning
Engineering and Physical Sciences Research CouncilFind out more...
- Aeronautical, Automotive, Chemical and Materials Engineering
- Mechanical, Electrical and Manufacturing Engineering
- Aeronautical and Automotive Engineering
Published inIEEE Transactions on Industrial Informatics
Pages7498 - 7507
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
- AM (Accepted Manuscript)
Rights holder© IEEE
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